compute_hindcast(hind, reference, metric='pearson_r', comparison='e2r', dim='init', max_dof=False, add_attrs=True, **metric_kwargs)¶
Compute a predictability skill score against a reference
- hind (xarray object) – Expected to follow package conventions:
init: dim of initialization dates *
lead: dim of lead time from those initializations Additional dims can be member, lat, lon, depth, …
- reference (xarray object) – reference output/data over same time period.
- metric (str) – Metric used in comparing the decadal prediction ensemble with the
- comparison (str) –
How to compare the decadal prediction ensemble to the reference:
- e2r : ensemble mean to reference (Default)
- m2r : each member to the reference
- dim (str or list) – dimension to apply metric over. default: ‘init’
- max_dof (bool) –
If True, maximize the degrees of freedom by slicing hind and reference to a common time frame at each lead.
If False (default), then slice to a common time frame prior to computing metric. This philosophy follows the thought that each lead should be based on the same set of initializations.
- add_attrs (bool) – write climpred compute args to attrs. default: True
- metric_kwargs (**) – additional keywords to be passed to metric (see the arguments required for a given metric in Metrics).
Predictability with main dimension
skill (xarray object)
- hind (xarray object) – Expected to follow package conventions: *